Correction: Schneider et al. A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM2.5 Concentrations across Great Britain. Remote Sens. 2020, 12, 3803

Rochelle Schneider; Ana M. Vicedo-Cabrera; Francesco Sera; Pierre Masselot; Massimo Stafoggia; Kees de Hoogh; Itai Kloog; Stefan Reis; Massimo Vieno; Antonio Gasparrini

doi:10.3390/rs13183588

,

and

¹

Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London WC1H 9SH, UK

²

The Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London WC1H 9SH, UK

³

Department of Forecasts, European Centre for Medium-Range Weather Forecast, Reading RG2 9AX, UK

⁴

Institute of Social and Preventive Medicine, University of Bern, 3012 Bern, Switzerland

Remote Sens.2021, 13(18), 3588;https://doi.org/10.3390/rs13183588

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In the original article [], there was a mistake in the legend for Figure 3—The legend contains wider colour ranges and it should be shorter. The correct legend appears below. The authors apologize for any inconvenience caused and state that the scientific conclusions are unaffected. The original article has been updated.

Figure 3. Stage-4 predicted PM_2.5 concentrations across Great Britain (Top) and London (Bottom) for 2008, 2013, and 2018 aggregated by annual means. All plots were built under the same colour scale.

Reference

Schneider, R.; Vicedo-Cabrera, A.M.; Sera, F.; Masselot, P.; Stafoggia, M.; de Hoogh, K.; Kloog, I.; Reis, S.; Vieno, M.; Gasparrini, A. A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM_2.5 Concentrations across Great Britain. Remote Sens. 2020, 12, 3803. [Google Scholar] [CrossRef] [PubMed]

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Correction: Schneider et al. A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM_2.5 Concentrations across Great Britain. Remote Sens. 2020, 12, 3803

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